Al-Hussayen, Anas2021-09-242021-09-242021-06https://hdl.handle.net/11299/224544University of Minnesota Ph.D. dissertation. June 2021. Major: Electrical Engineering. Advisor: Emad Ebbini. 1 computer file (PDF); ix, 84 pages.Medical ultrasound imaging systems continue to evolve at a fast pace enabled by advances in transducer technology, analog mixed-signal electronics, and high-performance computing. Ultrasound imaging is used in numerous applications, including echocardiography and peripheral vascular imaging in addition to specific anatomical imaging such as liver and kidney. In all these applications, ultrasound offers major advantages of portability, real-time visualization and measurements, and relative ease of use with and without the use of ultrasound contrast agents (UCA). One of the main limitations of this modality is the speckle phenomenon, which limits the contrast resolution. The contrast resolution is further compromised when imaging soft (low-scattering) tissue targets through or in the presence of strongly-scattering objects, which could introduce reverberation and/or clutter artifacts. Modern ultrasound employs ultrawideband transducers with fractional bandwidths larger than 70% being typical. This provides the opportunity to improve the image quality using frequency compounding methods, which have been traditionally used in speckle reduction at the expense of spatial resolution. However, this should be done with some understanding of the underlying scatterer spectrum in order to realize the promise of improved contrast. For example, in a uniform speckle region, almost any filterbank decomposition and compounding of the echo data results in an improved contrast ratio. For example, multiband audio signal processing is often based on the use of filterbanks with constant bandwidth B or constant quality factor Q. Such decomposition approaches are useful to gain insight into the spectrum characteristics. Complex scattering regions such as the surroundings of blood vessels may require a different approach for spectrum decomposition. This thesis research proposes the scattering spectrum matching (SSM) approach to the decomposition and compounding ultrasound echo data. The SSM approach seeks to decompose the echo signal spectrum based on an autoregressive (AR) model of the echo data rather than a preconceived filterbank design of the multiband signal processing architecture, e.g. constant B or constant Q. This statistical approach to spectrum decomposition accounts for the fact that the scattering spectrum is a `mixture' of scattering modes defined by the tissue organization in a given region of interest. It paves the way for adaptive learning algorithms for improving the specificity of medical ultrasound data. The SSM method is applied to medical ultrasound imaging of heterogeneous scattering from a peripheral artery with surrounding connective tissue and fat in addition to the intervening muscle tissue. Multiband analysis of the local spatial autocovariance reveals significant differences in the tissue organization in addition to reverberation components.ensignal processingultrasoundThesis or Dissertation